Development of conserved microsatellite markers of high cross-species utility in bat species (Vespertilionidae, Chiroptera, Mammalia)

Comparative ecological and behavioural studies of the widespread and diverse Vespertilionidae, which comprise almost 400 of the 1100 bat species, have been limited by the availability of markers. The potential of new methods for developing conserved microsatellite markers that possess enhanced cross-species utility has recently been illustrated in studies of birds. We have applied these methods to develop enhanced microsatellite markers for vespertilionid bats, in particular for the genus Myotis (103 species). We compared published bat microsatellites with their homologues in the genome sequence of the little brown bat, Myotis lucifugus, to create consensus sequences that were used to design candidate primer sets. Primer sets were then tested for amplification and polymorphism in 22 species of bat from nine of the largest families (including 11 Vespertilionidae). Of 46 loci tested, 33 were polymorphic, on average, in each of seven Myotis species tested, 20 in each of four species in other vespertilionid genera, and two in 11 nonvespertilionid species.     

西南鼠耳蝠(Myotis altarium)栖息生态学

西南鼠耳蝠(Myotis altarium)是典型的洞栖性蝙蝠,是我国的准特有种。近年,在湘西州境内的13个溶洞中记录到该蝠361只次,并对其栖点的空间分布、栖息姿势、体温、栖点温度及个体的安全性进行了观测。结果表明:其栖点主要集中在距离洞口200 m的范围之内,离地面的高度通常在2—6 m之间,约74%的个体采用腹部贴壁倒挂的姿势栖息;体温变化范围在3.57—20.25℃之间,个体体温均略高于其栖点温度,但两者之间不存在显著性差异(P0.05)而不同月份之间的体温、栖点温度均存在显著性差异(P0.05);仅60.1%的个体栖息在\"安全性高\"的栖点,栖息在\"安全性中\"和\"安全性低\"栖点的比例分别达22.7%和17.2%。加强对地方群众的宣传教育,增强民众对蝙蝠物种、生物多样性及洞穴资源的保护意识,减少人为因素对洞穴环境和蝙蝠种群的干扰与破坏,将有助于保护该珍稀物种。     

High‐grading bias: subtle problems with assessing power of selected subsets of loci for population assignment

Recognition of the importance of cross‐validation (‘any technique or instance of assessing how the results of a statistical analysis will generalize to an independent dataset’; Wiktionary, en.wiktionary.org) is one reason that the U.S. Securities and Exchange Commission requires all investment products to carry some variation of the disclaimer, ‘Past performance is no guarantee of future results.’ Even a cursory examination of financial behaviour, however, demonstrates that this warning is regularly ignored, even by those who understand what an independent dataset is. In the natural sciences, an analogue to predicting future returns for an investment strategy is predicting power of a particular algorithm to perform with new data. Once again, the key to developing an unbiased assessment of future performance is through testing with independent data—that is, data that were in no way involved in developing the method in the first place. A ‘gold‐standard’ approach to cross‐validation is to divide the data into two parts, one used to develop the algorithm, the other used to test its performance. Because this approach substantially reduces the sample size that can be used in constructing the algorithm, researchers often try other variations of cross‐validation to accomplish the same ends. As illustrated by Anderson in this issue of Molecular Ecology Resources, however, not all attempts at cross‐validation produce the desired result. Anderson used simulated data to evaluate performance of several software programs designed to identify subsets of loci that can be effective for assigning individuals to population of origin based on multilocus genetic data. Such programs are likely to become increasingly popular as researchers seek ways to streamline routine analyses by focusing on small sets of loci that contain most of the desired signal. Anderson found that although some of the programs made an attempt at cross‐validation, all failed to meet the ‘gold standard’ of using truly independent data and therefore produced overly optimistic assessments of power of the selected set of loci—a phenomenon known as ‘high grading bias.’